Last updated: 2018-11-23

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# scRNA-seq
library("scater")

# Matrices
library("Matrix")

# Plotting
library("cowplot")

# Presentation
library("glue")
library("knitr")

# Parallel
library("BiocParallel")

# Paths
library("here")

# Output
library("jsonlite")

# Tidyverse
library("tidyverse")
source(here("R/load.R"))
source(here("R/output.R"))

Introduction

In this document we are going to read in the Lindstrom human fetal kidney data, produce various quality control plots and remove any low-quality cells or uninformative genes.

genes.map <- read_tsv(here("data/genes.tsv"),
                      col_types = cols(
                          gene_id = col_character(),
                          gene_name = col_character()
                      )) %>%
    as.data.frame()

sce <- loadTSVSCE(here("data/Lindstrom/GSM2741551_count-table-human16w.tsv"),
                  genes.map,
                  dataset    = "Lindstrom",
                  org        = "human",
                  add.anno   = TRUE,
                  calc.qc    = TRUE,
                  calc.cpm   = TRUE,
                  pct.mt     = TRUE,
                  pct.ribo   = TRUE,
                  cell.cycle = TRUE,
                  sparse     = TRUE,
                  bpparam    = MulticoreParam(workers = 10),
                  verbose    = TRUE)

colData(sce)$Sample <- "1"

sce <- normalise(sce)
sce <- runPCA(sce)
sce <- runTSNE(sce)

write_rds(sce, here("data/processed/Lindstrom_SCE_complete.Rds"))

QC plots

By cell

Total counts

Violin plots of the library size (total counts) for each of the samples.

plotColData(sce, x = "Sample", y = "total_counts", colour_by = "Sample")

Number of features by library size

Relationship between the total counts for each cell and the number of expressed genes. We expect the number of genes to increase with the number of counts, hopefully reaching saturation.

plotColData(sce, x = "total_counts", y = "total_features", colour_by = "Sample")

PCA

PCA plots coloured by different variables.

p1 <- plotPCA(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotPCA(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotPCA(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotPCA(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotPCA(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotPCA(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

t-SNE

t-SNE plots coloured by different variables.

p1 <- plotTSNE(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotTSNE(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotTSNE(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotTSNE(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotTSNE(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotTSNE(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Explantory variables

Plots of the variance explained by various variables.

exp.vars <- c("Sample", "CellCycle", "log10_total_counts",
              "log10_total_features", "pct_dropout",
              "pct_counts_top_200_features", "PctCountsMT", "PctCountsRibo")
all.zero <- rowSums(as.matrix(counts(sce))) == 0
plotExplanatoryVariables(sce[!all.zero, ], variables = exp.vars)

Correlation between explanatory variables.

plotExplanatoryVariables(sce[!all.zero, ], variables = exp.vars,
                         method = "pairs")

RLE

Relative Log Expression (RLE) plots. Ideally all boxes should be aligned and have the size size.

plotRLE(sce[!all.zero], list(logcounts = "logcounts", counts = "counts"),
        c(TRUE, FALSE), colour_by = "Sample")

Mitochondrial genes

Looking at the effect of mitchondrial genes. We define mitochondrial genes as genes on the MT chromosome or with “mitochondrial” in the description.

plotColData(sce, x = "Sample", y = "PctCountsMT", colour_by = "Sample")

Ribosomal genes

Looking at the effect of ribosomal genes. We define ribosomal genes as genes with “ribosom” in the description.

plotColData(sce, x = "Sample", y = "PctCountsRibo", colour = "Sample")

Housekeeping genes

Plots of housekeeping genes. We may want to use these for filtering as a proxy for the health of the cell.

actb.id <- filter(data.frame(rowData(sce)), feature_symbol == "ACTB")[1, 1]
gapdh.id <- filter(data.frame(rowData(sce)), feature_symbol == "GAPDH")[1, 1]

key <- c("ACTB", "GAPDH")
names(key) <- c(actb.id, gapdh.id)
plotExpression(sce, c(actb.id, gapdh.id), colour_by = "Sample") + 
    scale_x_discrete(labels = key)

By gene

High expression

plotHighestExprs(sce)

Expression frequency by mean

plotExprsFreqVsMean(sce)

Total counts by num cells expressed

plotRowData(sce, x = "n_cells_counts", y = "log10_total_counts")

Filtering

colData(sce)$Filtered <- FALSE

To begin with we have 3745 cells with 32738 features from the ENSEMBL annotation.

Cells

Quantification

Let’s consider how many reads are assigned to features. We can plot the total number of counts in each cell against the number of genes that are expressed.

Cells that have been filtered are shown as triangles.

thresh.h <- 3500

plotColData(sce, x = "total_counts", y = "total_features",
            colour_by = "Sample", shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("Total counts") +
    ylab("Total features") +
    ggtitle("Quantification metrics")

Cells that express many genes are potential multiplets (multiple cells captured in a single droplet). We will remove 34 cells with more than 3500 genes expressed.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         colData(sce)$total_features > thresh.h

We now have 3711 cells.

Mitochondrial genes

Over-expression of mitochondrial genes can be an indication that a cell is stressed or damaged in some way. Let’s have a look at the percentage of counts that are assigned to mitchondrial genes.

thresh.h <- 8

plotColData(sce, x = "Sample", y = "PctCountsMT", colour_by = "Sample",
            shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("Sample") +
    ylab("% counts MT") +
    ggtitle("Mitochondrial genes")

Some of the cells show high proportions of MT counts. We will remove 212 cells with greater than 8% MT counts.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         colData(sce)$PctCountsMT > thresh.h

That leaves 3504 cells.

Ribsomal genes

We can do a similar thing for ribosomal gene expression.

thresh.h <- 35

plotColData(sce, x = "Sample", y = "PctCountsRibo", colour_by = "Sample",
            shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("Sample") +
    ylab("% counts ribosomal") +
    ggtitle("Ribosomal genes")

Some of the cells show high proportions of ribosomal counts. We will remove 61 cells with greater than 35% ribosomal counts.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         colData(sce)$PctCountsRibo > thresh.h

That leaves 3444 cells.

Housekeeping

Similarly we can look at the expression of the “housekeeping” genes GAPDH and ACTB.

thresh.h <- 1
thresh.v <- 1.5

plotExpression(sce, gapdh.id, x = actb.id, colour_by = "Sample",
               shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    geom_vline(xintercept = thresh.v, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("ACTB") +
    ylab("GAPDH") +
    ggtitle("Housekeepking genes") +
    theme(
        strip.background = element_blank(),
        strip.text.x = element_blank()
    )

We will remove cells where ACTB is expressed below 1.5 or GAPDH is expressed below 1. This removes 322 cells.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         exprs(sce)[actb.id, ] < thresh.v |
                         exprs(sce)[gapdh.id, ] < thresh.h
sce <- sce[, !colData(sce)$Filtered]

After filtering we are left with 3178 cells.

Dimensionality reduction (filtered cells)

Now that we are relatively confident we have a set of good quality cells, let’s see what they look like in reduced dimensions.

PCA

sce <- runPCA(sce)

p1 <- plotPCA(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotPCA(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotPCA(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotPCA(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotPCA(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotPCA(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

t-SNE

sce <- runTSNE(sce)

p1 <- plotTSNE(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotTSNE(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotTSNE(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotTSNE(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotTSNE(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotTSNE(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Genes

Expression

Some of the features we would never expect to see expressed in an RNA-seq experiment. Before doing anything else we will remove the features that have less than two counts across all cells.

keep <- rowSums(counts(sce)) > 1
sce <- sce[keep]

This removes 14227 genes and leaves us with 18511.

We will also going remove genes that are expressed in less than two individual cells.

keep <- rowSums(counts(sce) != 0) > 1
sce <- sce[keep, ]

This removes 30 genes and leaves us with 18481.

HGNC genes

We are also going to filter out any genes that don’t have HGNC symbols. These are mostly pseudogenes and are unlikely to be informative.

keep <- !(rowData(sce)$hgnc_symbol == "") &
        !(is.na(rowData(sce)$hgnc_symbol))
sce <- sce[keep, ]

This removes 2315 genes and leaves us with 16166.

dups <- which(duplicated(rowData(sce)$feature_symbol))

There are 0 gene(s) with duplicate HGNC symbol names. For these genes we will use an alternative symbol name. Once we have done this we can rename the features using feature symbols instead of ENSEMBL IDs which will make interpreting results easier.

rowData(sce)[dups, "feature_symbol"] <- rowData(sce)[dups, "symbol"]
rownames(sce) <- rowData(sce)$feature_symbol

Dimensionality reduction (filtered genes)

Let’s see what our final dataset looks like in reduced dimensions.

PCA

sce <- runPCA(sce)

p1 <- plotPCA(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotPCA(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotPCA(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotPCA(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotPCA(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotPCA(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

t-SNE

sce <- runTSNE(sce)

p1 <- plotTSNE(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotTSNE(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotTSNE(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotTSNE(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotTSNE(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotTSNE(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

We now have a high-quality dataset for our analysis with 16166 genes and 3178 cells. A median of 1509.5 genes are expressed in each cell.

Summary

Parameters

This table describes parameters used and set in this document.

params <- toJSON(list(
    list(
        Parameter = "total_features",
        Value = 3500,
        Description = "Maximum threshold for total features expressed"
    ),
    list(
        Parameter = "mt_counts",
        Value = 8,
        Description = "Maximum threshold for percentage counts mitochondrial"
    ),
    list(
        Parameter = "ribo_counts",
        Value = 35,
        Description = "Maximum threshold for percentage counts ribosomal"
    ),
    list(
        Parameter = "ACTB_expr",
        Value = 1.5,
        Description = "Minimum threshold for ACTB expression"
    ),
    list(
        Parameter = "GAPDH_expr",
        Value = 1,
        Description = "Minimum threshold for GAPDH expression"
    ),
    list(
        Parameter = "n_cells",
        Value = ncol(sce),
        Description = "Number of cells in the filtered dataset"
    ),
    list(
        Parameter = "n_genes",
        Value = nrow(sce),
        Description = "Number of genes in the filtered dataset"
    ),
    list(
        Parameter = "median_genes",
        Value = median(colSums(counts(sce) != 0)),
        Description = paste("Median number of expressed genes per cell in the",
                            "filtered dataset")
    )
), pretty = TRUE)

kable(fromJSON(params))
Parameter Value Description
total_features 3500 Maximum threshold for total features expressed
mt_counts 8 Maximum threshold for percentage counts mitochondrial
ribo_counts 35 Maximum threshold for percentage counts ribosomal
ACTB_expr 1.5 Minimum threshold for ACTB expression
GAPDH_expr 1 Minimum threshold for GAPDH expression
n_cells 3178 Number of cells in the filtered dataset
n_genes 16166 Number of genes in the filtered dataset
median_genes 1509.5 Median number of expressed genes per cell in the filtered dataset

Output files

This table describes the output files produced by this document. Right click and Save Link As… to download the results.

write_rds(sce, here("data/processed/Lindstrom_SCE_filtered.Rds"))
dir.create(here("output", DOCNAME), showWarnings = FALSE)

write_lines(params, here("output", DOCNAME, "parameters.json"))

kable(data.frame(
    File = c(
        glue("[parameters.json]({getDownloadURL('parameters.json', DOCNAME)})")
    ),
    Description = c(
        "Parameters set and used in this analysis"
    )
))
File Description
parameters.json Parameters set and used in this analysis

Session information

devtools::session_info()
 setting  value                       
 version  R version 3.5.0 (2018-04-23)
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 tz       Australia/Melbourne         
 date     2018-11-23                  

 package              * version   date      
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 codetools              0.2-15    2016-10-05
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 data.table             1.11.4    2018-05-27
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 zlibbioc               1.26.0    2018-07-30
 source                            
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 local                             
 CRAN (R 3.5.0)                    
 cran (@0.1.1)                     
 cran (@0.2.2)                     
 Bioconductor                      
 Bioconductor                      
 Bioconductor                      
 cran (@1.0-6)                     
 cran (@0.5.0)                     
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@1.3-2)                     
 local                             
 cran (@0.9.3)                     
 CRAN (R 3.5.0)                    
 cran (@1.11.4)                    
 local                             
 Bioconductor                      
 Bioconductor                      
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@0.7.6)                     
 Bioconductor                      
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 Bioconductor                      
 Bioconductor                      
 Bioconductor                      
 CRAN (R 3.5.0)                    
 cran (@3.0.0)                     
 CRAN (R 3.5.0)                    
 cran (@1.3.0)                     
 local                             
 local                             
 local                             
 cran (@2.3)                       
 cran (@0.2.0)                     
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@1.4.5)                     
 CRAN (R 3.5.0)                    
 Bioconductor                      
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@0.7.3)                     
 CRAN (R 3.5.0)                    
 cran (@0.2.1)                     
 Bioconductor                      
 CRAN (R 3.5.0)                    
 cran (@1.7.4)                     
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 local                             
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@0.5.0)                     
 CRAN (R 3.5.0)                    
 local                             
 cran (@1.3.0)                     
 cran (@2.0.1)                     
 cran (@1.8.4)                     
 cran (@1.0.1)                     
 cran (@0.2.5)                     
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@0.12.18)                   
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@1.4.3)                     
 Bioconductor                      
 Bioconductor                      
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 Github (rstudio/rmarkdown@18207b9)
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 Bioconductor                      
 cran (@0.5.0)                     
 Bioconductor                      
 cran (@1.1.0)                     
 CRAN (R 3.5.0)                    
 Bioconductor                      
 local                             
 local                             
 cran (@1.2.4)                     
 CRAN (R 3.5.0)                    
 Bioconductor                      
 cran (@1.4.2)                     
 cran (@0.8.1)                     
 cran (@0.2.4)                     
 CRAN (R 3.5.0)                    
 local                             
 Bioconductor                      
 local                             
 CRAN (R 3.5.0)                    
 cran (@0.5.1)                     
 cran (@0.3.0)                     
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 CRAN (R 3.5.0)                    
 cran (@1.8-2)                     
 Bioconductor                      
 cran (@2.2.0)                     
 Bioconductor

This reproducible R Markdown analysis was created with workflowr 1.1.1